Conference proceeding
Knee Joint Torque Prediction with Uncertainties by a Neuromusculoskeletal Solver-informed Gaussian Process Model
2023 International Conference on Advanced Robotics and Mechatronics (ICARM), pp.1035-1040
07/08/2023
DOI: 10.1109/ICARM58088.2023.10218934
Abstract
Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is rapidly growing, yet uncertainty in predicted torque capacity can significantly impact the user-exoskeleton interface safety. In this paper, we estimated knee flexion/extension torques by using a neuromusculoskeletal (NMS) solver-informed Gaussian process (NMS-GP) model with muscle electromyography signals and joint kinematics as model inputs. The NMS-GP model combined the NMS and GP models by integrating valuable features from an NMS solver into a GP model. The NMSGP model was used to predict knee joint torque in daily activities with uncertainty quantification. The activities included slow walking, self-selected speed walking, fast walking, sit-to-stand, and stand-to-sit. Model performance, defined as low prediction error between the model's predicted torque and measured torques from inverse dynamics computations, of both the NMS-GP and NMS models was analyzed. We found that prediction error was significantly lower in NMS-GP models than in NMS models. We observed relatively high uncertainties in the predicted knee torque at the beginning of each movement, particularly in self-selected speed walking. High uncertainties were also found during terminal stance and swing in self-selected speed walking. Compared to other torque prediction methods, the proposed NMS-GP model not only provides an accurate joint torque prediction but also a measure of the uncertainty. Our study showed that the NMS-GP model has a large potential in control strategy design for rehabilitation exoskeletons and to enhance the overall user experience.
Details
- Title: Subtitle
- Knee Joint Torque Prediction with Uncertainties by a Neuromusculoskeletal Solver-informed Gaussian Process Model
- Creators
- Longbin Zhang - KTH Royal Institute of TechnologyXiaochen Zhang - KTH Royal Institute of TechnologyXueyu Zhu - University of IowaRuoli Wang - KTH Royal Institute of TechnologyElena M. Gutierrez-Farewik - KTH Royal Institute of Technology
- Resource Type
- Conference proceeding
- Publication Details
- 2023 International Conference on Advanced Robotics and Mechatronics (ICARM), pp.1035-1040
- Publisher
- IEEE
- DOI
- 10.1109/ICARM58088.2023.10218934
- Grant note
- 504054 / Simons Foundation (10.13039/100000893) A22078,18014,18200,21302 / Promobilia Foundation (10.13039/100009389) 2018–00750,2018–04902 / Swedish Research Council (10.13039/501100004359)
- Language
- English
- Date published
- 07/08/2023
- Academic Unit
- Mathematics
- Record Identifier
- 9984459655602771
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